The radiotherapy treatment of a cancer patient may commence typically couple of weeks after a pre-treatment imaging procedure. The pre-treatment imaging procedure simulates a treatment scene where the patient is transported into a specific position inside a gantry of a radiation device. Further, one or more images of the patient are obtained for the doctors or radiologists to determine an original treatment plan. However, the anatomy of the patient may change after the pre-treatment imaging procedure or during the treatment procedure. Based on the amount of change in the anatomy, the original treatment plan may need to be modified in order to reduce the toxicity and improve targeting of the tumor and the overall outcome of the treatment. One of the fundamental tasks during a replanning phase is segmenting and contouring of different targets and organs at risk. Many automated techniques have been developed to provide an initial approximation of the targets and organs at risk as well as to automate the contouring task to a large extent. However, the performance of the automated contouring techniques relies on the availability and the amount of pre-labeled and segmented image data. As the labeled segmented image data is often not available in a large scale, there is a need to generate the labeled segmented data in large scale to improve the performance of automated contouring for the radiotherapy replanning.